As Magenta progresses, I predict that machine learning will become a part of the toolkit for artistic expression, including music and art.

When creating Magenta, I think about the division between engineering, research, and artistry, aiming to build interesting ways to make new kinds of art rather than just being a machine learning artist.

What's interesting about a project like Magenta is that it serves as a tool for creativity, not a replacement for human artists.

To become a creative coder, visit our site at g.co/magenta, where you can find resources and contribute to our open-source projects.

I think the creative applications of machine learning are fascinating and present opportunities for artists to explore new forms of expression.

The creative spirit of humanity is crucial, and we aim to build tools that enhance creativity, enabling people to create better art and content.

Meta is developing AI tools for coding and research, aiming to automate software engineering tasks, which could lead to a significant increase in productivity and innovation in AI development.

We're rethinking our music sequence generation work, moving beyond simple models to care about expressive timing, dynamics, and polyphony, aiming to create high-quality music generation tools.

The goal is to create tools that allow composers to offload interesting aspects of their work, like expressive timing and musical texture.